Predicting temperatures inside a steel slab reheating furnace using Deep Learning

Abstract

Due to the complexity and high financial costs involved in production processes, the steel industry can benefit from applications of intelligent systems, capable of performing automated activities. This research paper addresses a description of the process of creating a data-driven computational system to develop a computational thermal model of a real steel plate reheating furnace. Sufficiently accurate computational models can be used in conjunction with combustion control optimization techniques, such as model-based predictive control (MPC), or even a Digital Twin of the combustion system of a plate reheating furnace. The tool can be used in predictive failure diagnosis, fundamental for the maintenance and operation teams responsible for asset management. For this development, Recurrent Artificial Neural Networks have been widely applied, validating the existence of series that have temporal links between their samples, a typical case of monitoring industrial process variables. To meet the proposed objective, the performance of models based on recurrent neural networks of the Long Short Term-Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolutional Network (TCN) type was analyzed. The results were evaluated under different prediction horizons, since such techniques demand models capable of accurate predictions that are several steps ahead, premised on prediction capability.

DOI:https://doi.org/10.56238/alookdevelopv1-016